发表机构
PayPal, Inc.（贝宝公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对企业MCP网关的上下文饱和与工具发现难题，提出SCOUT方案，通过混合检索的MCP元工具实现优化，在PayPal生产环境中大幅降低工具令牌消耗与推理成本。
AI 中文摘要
大型语言模型（LLM）智能体调用外部工具以检索和推理预训练知识之外的信息。模型上下文协议（MCP）规范了此类工具的呈现方式，代理MCP服务器将多个后端服务器聚合到单一端点后，为身份验证、策略执行和可观测性提供了安全、可管控的枢纽。该架构引发两个相互叠加的挑战：一是上下文工程瓶颈，即完整工具模式会在任何用户查询前就占满模型上下文窗口；二是工具可发现性障碍，即用户和智能体无法从200多个MCP服务器索引的2000多个工具中识别出最佳工具。提示缓存可降低重复处理成本，但既无法释放上下文容量，也无法提升准确性。我们提出SCOUT（通用工具的选择性上下文优化），将工具暴露重新定义为上下文选择问题，仅注入当前步骤相关的工具。SCOUT提供两个MCP元工具——tool_search和execute_tool，其中tool_search执行混合检索，通过互反秩融合将BM25稀疏匹配与密集向量搜索融合，返回前k个相关工具。依托零停机目录更新管道，SCOUT解决了上下文饱和与工具发现两大挑战。在PayPal的生产环境中，SCOUT将MCP工具令牌消耗从140.2k令牌（占上下文的70.1%）降至1.3k令牌（占0.8%），降幅达99%，降低了企业规模下的单查询推理成本。由于SCOUT以标准MCP工具形式呈现，它与模型无关且无需客户端修改。
英文摘要
Large language model (LLM) agents invoke external tools to retrieve and reason over information beyond pretrained knowledge. The Model Context Protocol (MCP) standardizes how such tools are surfaced, and a proxy MCP server aggregates many backend servers behind a single endpoint providing a secure, governable chokepoint for authentication, policy enforcement, and observability. This architecture creates two compounding challenges: a context-engineering bottleneck where full tool schemas saturate the model context window before any user query, and a tool discoverability barrier where users and agents cannot identify the best tool among 2,000+ indexed tools across 200+ MCP servers. Prompt caching reduces reprocessing cost but neither frees context capacity nor improves accuracy. We present SCOUT (Selective Context Optimization for Universal Tooling), which reframes tool exposure as a context-selection problem, injecting only tools relevant to the current step. SCOUT surfaces two MCP meta-tools -- tool_search and execute_tool -- where tool_search performs hybrid retrieval, fusing BM25 sparse matching with dense vector search via Reciprocal Rank Fusion to return the top-k relevant tools. Backed by zero-downtime catalog update pipelines, SCOUT resolves both context saturation and tool discovery challenges. In production at PayPal, SCOUT reduces MCP tool-token consumption from 140.2k tokens (70.1% of context) to 1.3k tokens (0.8%), a 99% reduction, cutting per-query inference cost at enterprise scale. Because SCOUT is surfaced as standard MCP tools, it is model-agnostic and requires no client-side modifications.
